Data Manipulation and Transformation Tutorials

The write_xlsx() from writexl library used to export data frame to excel file. The following method shows how you can do it with syntax. Method: Use write_xlsx() From writexl Package library("writexl") write_xlsx(dataframe,"file_path") The following example shows how to use write_xlsx() function in R. Export Data Frame to Excel File Using write_xlsx() Function in R To use write_xlsx() function you need to follow below steps: Step 1: Create a Data Frame Let’s first start with creating data frame:

There are multiple ways to filter rows based on condition in R data frame. The following methods show how you can do it with syntax. Method 1: Use filter() Function library(dplyr) df %>% filter(column > n) Method 2: Use Logical Indexing df[df$column > n, ] Method 3: Use subset() Function subset(df, column > n) The following examples show how to filter rows based on condition in a dataframe. Using filter() Function Let’s see how we can use filter() function from dplyr package:

To add count column to data frame in R, you can use add_count() function from dplyr package. The following method shows how you can do it with syntax. Method 1: Use add_count() Function library(dplyr) df %>% add_count(column1, column2, name = "count") The following example shows how to add count column to dataframe in R. Using add_count() Function Let’s see how we can add count column to dataframe using add_count() function:

To calculate percentage by group in R, you can use group_by() and mutate() function from dplyr package. The following method shows how you can do it with syntax. Method: Use dplyr Package library(dplyr) dataframe %>% group_by(column1) %>% mutate(percent = column2/sum(column2)) The following example shows how to calculate percentage by group in R. Use dplyr Package to Calculate Percentage by Group Let’s see how we can calculate percentage by group in R:

To calculate quantiles by group in R, you can use quantile() function from dplyr package. The following method shows how you can do it with syntax. Method: Use quantile() Function library(dplyr) df %>% group_by(column1) %>% summarize(quant25 = quantile(column2, 0.25, quant50 = quantile(column2, 0.50), quant75 = quantile(column2, 0.75), quant100 = quantile(column2, 1.0)) The following example shows how to calculate quantile values by group in R. Using quantile() Function Let’s see how we can calculate quantile values of column of of data frame:

To calculate rolling correlation in R, you can use rollapply() function from zoo package. The Rolling correlation is basically Rolling correlations are correlations between two time series on a rolling window. The following method shows how you can do it with syntax. Method: Use rollapply() Function library(zoo) rollapply(df, width=3, function(x) cor(x[,2],x[,3]), by.column=FALSE) The following example shows how to calculate rolling correlation in R. Use rollapply() to Calculate rolling correlation Let’s see how we can calculate rolling correlation between column of data frame:

To ckeck which package version is loaded in R, you can use packageVersion() function. The following method shows how you can do it with syntax. Method: Use packageVersion() Function packageVersion("Package_name") The following example shows how to use packageVersion() function in R. Using packageVersion() Function Let’s see how we can use packageVersion() function to check version of package: # Get version package packageVersion("ggplot2") Output: [1] ‘3.4.4’ Here the output shows package version of ggplot2 package.

To count TRUE values in logical vector in R, you can use sum() function. The following method shows how you can do it with syntax. Method: Use sum() Function sum(vector) The following example shows how to count TRUE valeus from logical vector in R. Using sum() Function Let’s see how we can use sum() function to count TRUE values in logical vector: # Define vector Status <- c(TRUE,TRUE,FALSE,TRUE,FALSE,FALSE,TRUE,FALSE) # Count TRUE value s <- sum(Status) # Print count print(s) Output:

To count unique values by group in R, you can use two different function one way is group_by() and summarise() function from dplyr package. The other way to use unique() and length() function data.table package. The following methods show how you can do it with syntax. Method 1: Use dplyr Package library(dplyr) df %>% group_by(group_columm) %>% summarise(count = n_distinct(values_column)) Method 2: Use data.table Package library(data.table) df[, .(count = length(unique(values_column))), by = group_column] The following examples show how to count uniqque values by group in R.

To count unique values from column of data frame in R, there are two different methods one is length() function with unique() function and other is summarise() function from dplyr package. The following methods show how you can do it with syntax. Method 1: Use length() and unique() Functions length(unique(df$column_name)) Method 2: Use summarise() Function library(dplyr) df %>% summarise(count=n_distinct(df$column_name)) The following examples show how to count unique values from column of data frame in R.

To count the value in column with condition in R, you can use two different ways one is using nrow() function and other is using summarise() function from dplyr package. The following methods show how you can do it with syntax. Method 1: Use nrow() Function nrow(df[df$column_name == value, ]) Method 2: Use summarise() Function df %>% filter(column_name == value) %>% summarise(count = n()) The following examples show how to count value in column with condition in R.

To create a frequency table of multiple variables in R, you can use table() function and summarise() function from dplyr package. The following methods show how you can do it with syntax. Method 1: Use table() Function table(df$column1, df$column2) Method 2: Use dplyr Package library(dplyr) df %>% group_by(df$column1, df$column2) %>% summarise(count = n()) The following examples show how to create frequency table of multiple variables in R. Create Frequency Table of Multiple Variables Using table() Function in R Let’s see how we can use table() function in R: